Particulate Matter/Number Synchronization Measurement Device

ABSTRACT

An emissions measurement system capable of providing an accurate, real-time estimate of particle number (PN)/particulate matter (PM) within exhaust is disclosed. The system is capable of accurately differentiating the size and composition of PM/PN by synchronizing dissimilarly configured sensors. The exhaust may be generated by an internal combustion engine, in which case the system may be sequentially connected to the exhaust from the internal combustion engine.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/512,266 filed Mar. 17, 2017, which is a national stage application ofPCT/US2015/050950 filed Sep. 18, 2015, which claims priority to U.S.App. No. 62/052,525 filed Sep. 19, 2014, the entire disclosures of whichare hereby incorporated by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to an improved particulatematter (“PM,” or the total mass of the particulate contained in asample) and/or particle number (“PN,” or the total number of particlesthat comprise the particulate contained in a sample) measurement device.

BACKGROUND OF THE DISCLOSURE

Vehicle and transportation sector-related emissions continue to be aleading source of greenhouse gas (GHG) and air pollution in urban areasaround the globe. As an example, there were over 260 million vehicles inthe United States (U.S.) in 2012 that emitted 33% (1,750 million metrictons) of total U.S. carbon dioxide (CO₂) emissions. In the same year,the U.S. transportation sector share of total U.S. emissions for carbonmonoxide (CO), nitrogen oxides (NO_(x)) and PM were 54%, 59%, and 8%,respectively. Therefore, significant resources continue to be focused onemission reduction tactics which typically fall into two categories:current fleet inventory upgrade (e.g., roadside and/or engine bayinspection and maintenance (I/M) programs, aftermarketengine/vehicle/fuel programs, etc.) and new vehicle manufacturing (e.g.,revisions of emissions control standards for newly manufacturedvehicles, etc.).

The U.S. Environmental Protection Agency (USEPA) defines airborneparticulate as “a complex mixture of extremely small particles andliquid droplets . . . made up of a number of components, including acids(such as nitrates and sulfates), organic chemicals, metals, and soil ordust particles.” In recent decades, the USEPA, like many otherenvironmental regulatory agencies, has invested significant time,effort, and resources in a wide range of particulate monitoringactivities. Early vehicle exhaust particulate monitoring programsemployed relatively crude gravimetric methods such as capturingparticulate emissions samples on pre-cleaned and weighed filter papersand re-weighing these post-sampling to determine PM mass by difference.

Despite the highly labor-intensive nature of these methods, refinementsof these early gravimetric methods (typically employing more inertfilter substrates) are still widely used even today. However,significant time and effort has also been focused on the development ofother particulate monitoring techniques, most notably methods that couldbe automated to increase quantitative accuracy, increase samplingefficiency, reduce sampling errors, and reduce associated costs. In morerecent years these automated methods have become the basis of morerecent rounds of environmental particulate regulation.

The earliest automated particulate measurement technologies were basedon measurement principles that provided mass measures or measures thatcould be readily calibrated against mass measures. Two examples aretapered element oscillation microbalance (TEOM) methods used for routineautomatic monitoring of adherence to ambient PM air quality standardsand opacity-based methods used in vehicle exhaust PM emissions TIMinspection testing procedures.

This early focus on PM may have reflected both a design to align thesenewer methods with more established gravimetric testing procedures andan acknowledgement that these measurement technologies were likely themost convenient and accurate options at the time. However, asparticulate sources became cleaner in response to PM-focused regulationsand associated consumer demand, investigators observed that emissionsource particulate size distributions tended to become finer and thatthe number of ultra-fine particles in ambient air is more closelycorrelated with health effects than the total mass of those particles.

More recently, the development of a PN measurement system by theEuropean Union in 2007 was to enable a more accurate and repeatableidentification process of particulates (individual emitted particles).Additional EU objectives were to minimize required changes to thecurrent type approval facilities such as laboratories, to employ anunderstandable metric, and for the system to be simple to operate. TheEU PN system was developed to avoid the possible requirement forcorrection factors, an issue that has hampered the development ofsimilar measurement initiatives in the U.S.

Accurate emission(s) data are required for a wide range of vehicle andpoint-source related applications in order to properly evaluate theimpact of emission reduction strategies. However, given the complex and(perhaps more importantly) evolving nature of particulate emissions, asingle PM or PN metric is unlikely to provide a robust “catch-all”metric for emission-reduction activities. Therefore, it is important todifferentiate between the differing sizes of PM/PN in order to bothbetter understand the process that produced them and aid in theidentification of potential solution(s) for their reduction. Atmosphericparticulate sizes typically range from a few nanometers to tens ofmicrometers in diameter. The coarsest material (typically 10 micrometersand larger) is predominantly from biological sources (e.g., spores,pollen, bacteria, etc.) and/or mineral sources (e.g., land erosion,construction work, etc.). Finer particles (less than a micrometer) aretypically formed by nucleation, condensation, and agglomerationprocesses, such as a result of atmospheric chemistry and combustionprocesses.

PM/PN that originates from combustion processes are typically ofinterest.

Particles smaller than 0.1 micrometer (“ultra-fines”) formed as theresult of fuel combustion/exhaust emissions processes are associatedprimarily with internal combustion engines, such as those in on-road,off-road, and non-road vehicle activities. Such particulates have beencited as dangerous due to toxic trace compounds (e.g., heavy metals,polycyclic aromatic hydrocarbons, etc.) often contained in theparticulates. The USEPA and the European Union's Joint Research Centre(JRC) have both declared that the concentration of such particles ishighly variable, and appears to demonstrate a significant pattern ofvariation, especially close to urban areas and traffic congestion.

Traditionally, vehicle testing is often performed in a laboratory with achassis or engine dynamometer, following government-approved testingcycles (e.g., Euro IV, U.S. FTP, or other global standards). However,these standard test procedures, much like the traditional gravimetric PMmeasurement procedures, are no longer representative of their real-worldcounterparts and the growing gap between the emission reductions andfuel savings routinely achieved by modern cars on these test cycles andtheir on-road (or “off cycle”) performance has been widely reported.

Investigators already widely acknowledge the “off-cycle” gap to be ofthe order of 40-60% for fuel consumption and CO₂ emissions, and up to400% (4 times approval levels) for NO_(x) emissions. Although thechallenges associated with real world measurement of vehicle PM/PNemissions using current technology significantly hinder the directmeasurement of similar trends for particulates, indirect measurementmethods, most notably tunnel studies, indicate that these also may beorders of magnitude higher than lab-based measurements.

Concerns about these “off-cycle” emissions have led to an increaseddemand for real-world vehicle emission data and for new regulationsbased on real-world vehicle performance.

Portable Emissions Measurement Systems (PEMS) are vehicle monitoringplatforms that can be temporarily attached to a target vehicle toprovide a direct measure of vehicle emissions as the vehicle is used inactual service. For example, the “Real-time On-road Vehicle EmissionsReporter” (ROVER) disclosed by the USEPA is an on-board testing systemtemporarily mounted on a vehicle for the purposes of measuringreal-world emissions while the vehicle is driven on the roadway.Commercial systems, many based on the original ROVER concept, are nowused to collect real-world vehicle emission data.

However, the disadvantages of the conventional PEMS approach are alsosignificant. Due to the fact that all commercially available PEMSequipment requires that the sample be transported well away from theexhaust stack or tailpipe, issues such as heat changes, sampledegradation, power requirements, and system complexity typicallyintroduce accuracy and dependability problems. These issuessignificantly impact the investigation of emission reduction tactics, asPEMS are limited by their design, size, and weight, and thereby limitthe amount of real-world vehicles and test data that can be collected ina “true” real-world scenario. Simply put, PEMS-related activities(equipment installation, maintenance, upkeep, charging, filterreplacements, supervised operation, uninstall, etc.) often hinder theability to collect truly representative “real-world” data in sufficientvolumes and/or at reasonable costs.

Present vehicle exhaust PM/PN PEMS have significant power demandsattributable to the sensor design, sample dilution, and flowmeasurement(s). Due to these challenges, currently-available PEMS do nothave the capability to run self-powered for more than a few hours or, insome cases, even more than a few minutes, which significantly restrictssampling options. Also, the accompanying weight of such systems demandsthat the device(s) be mounted well away from the exhaust outlet, furtherlimiting the target vehicle's performance under typical drivingconditions. In addition, the associated use of long sample line(s)required to transport the sample to the sensors introduces a range ofsample integrity issues that particulates are particularly sensitive to.

One problem is that increased power to heat or condition the samplewhile it is being transferred from the exhaust to the sensor addssignificantly to the power requirements and weight of the PEMS unit.Additionally, the increased length of the sample line(s) increases thesurface area for interaction with the sample (e.g., water vaporcondensation and particulate deposition), which, in turn, must beaccounted for and expelled/corrected.

There are several problems with increased length of sample tubes and/orlines that PM/PN measurements are particularly sensitive to.

First, the additional length means an increase in the power required topump a sample at a given flow rate due to flow friction, which increaseswith the length of the sample tubing, and necessitates a more powerfulpump and a need for a larger, more powerful battery.

Second, in addition to the direct increase to the size and weight of thetesting device, longer sample lines introduce additional weight, bulk,and complexity to the testing process. They must be properly and safelyclamped, secured, or tied along the entire run of sample line. Thisincreases the chance of safety issues, such as improperly secured linesthat get caught up in running machinery and moving parts, etc. Lengthnegatively affects the reliability of the system. The increased lengthof the sample line(s) also increases the surface area for particulatedeposition, which, in turn, must be accounted for and corrected.

Third, a longer length of sample tubing requires a proportionallyincreased amount of insulation and/or supplied heat to prevent thecondensation of liquid water and deterioration of the sample as ittravels to the testing device and cools. Most exhaust gas samplescontain significant amounts of water. If this is allowed to condenseduring sample transfer or analysis, the resulting liquid water caninteract with and degrade some pollutants, most notably the morereactive gaseous species and particulates. However, the increasedinsulation adds significant weight and bulk to the sample lines, and inmany cases PEMS units require additional dedicated power supplies forthe sample line(s) heaters.

Fourth, as sample line length increases, it also increases the amount ofsetup/teardown time required to perform testing. The additional timerequired to properly install long sample lines directly impacts thenumber of accurate and safe tests that can be completed in a shift.

Finally, another problem with present vehicle PM/PN PEMS is their focuson a single sensing method. Although these current PM/PN systems havebeen accepted as accurate by various federal, state, local, and globalevaluation standards, a single measurement method-based solution istypically advocated by regulatory agencies. Extremely accurate sensingof one variable may require equipment of large size and weight. It alsomeans that associated reliability of any analyte measurement isintrinsically linked to one measurement principle and, therefore,requires the continued representativeness of the associated metric.

Each sensing technique uses a different approach and has a differentbias with the PM/PN that is being sensed and recorded. Unlike gaseouspollutants, such as CO₂ or NO_(x), PM/PN is not one chemical species.The exact constitution of PM/PN emissions includes complex structures,for example a solid phase carbon particle with liquid phase hydrocarbonsadsorbed onto its surface, and both of these phases can incorporate,adsorb, or absorb numerous species in numerous distributions.Furthermore, PM/PN exists in a wide range of sizes, and health concernshave been associated with PM/PN of aerodynamic diameter from 10micrometers to less than 100 nanometers. Any one measurement techniquewill provide results that are biased by the type of PM/PN themeasurement technique is most sensitive to and no one measurementtechnique can be sensitive to the complete range of PM/PN chemical andphysical structures. Thus, PM/PN, by its very nature, cannot be fullycharacterized by any one sensing technique, however accurate it may be.

This point is illustrated by considering the existing CaliforniaHeavy-Duty I/M test procedure. This incorporates a measurement based onopacity (a measure of light extinction). When this was first introduced,it was a highly effective test because it provided a good measure of thelarger coarser material that then represented a significant fraction ofexhaust PM mass. More recent improvements to vehicle engine managementsystems and exhaust emissions abatement systems have both reduced theamount of particulate emitted by vehicles and the size ranges it istypically emitted in. The opacity method is not sensitive to the smalleramounts of finer material that modern vehicle typically produce. As aresult, a faulty modern vehicle can emit large amounts of particulates,often well above regulatory limits, but still pass an FM test becausethe emitted particulate is too fine to be detected using opacity.

What is needed is an improved PM and PN measurement device that is botheasier to deploy (e.g., smaller, lighter weight, lower energy demand)and provides an ability to provide both a measure of PM/PN on the basisof current standards and also identify, characterize, or map onto thechanging properties of particulates as their emission sources change.

BRIEF SUMMARY OF THE DISCLOSURE

In a first embodiment, an emissions measurement system is provided. Theemissions measurement system includes an emissions sample inlet, atleast three sensors connected to the emissions sample inlet, and anemissions sample outlet connected to the at least three sensors. Thesensors are sequentially connected in a linear arrangement. Each of theat least three sensors is configured to perform a different measurementof a sample.

Each of the sensors may be selected from the group consisting of, forexample, a laser light opacity sensor, a light scattering sensor, aparticle ionization sensor, a particle acoustic measurement sensor, andan electrostatic precipitation sensor. In an instance, the sensorsinclude a laser light opacity sensor, a light scattering sensor, and aparticle ionization sensor. One of the sensors may be a laser lightopacity sensor configured to use a blue laser.

The sensors may be configured to be synchronized. A processing unit maybe wirelessly connected to the sensors. The processing unit may beconfigured to provide results based on data provided by the sensors. Theprocessing unit may be configured to triangulate the data provided bythe sensors.

In a second embodiment, a method of measuring emissions is provided. Themethod includes linearly transporting an emissions sample through atleast three sensors and calculating either a particle number (PN) orparticulate matter (PM) measurement for the emissions sample using datafrom the sensors. Each of the sensors is configured to perform adifferent measurement of the emissions sample.

The method may further include triangulating the data from the at leastthree sensors. The calculating may use a proportionality factor, aweighted linear integral factor, or a non-linear integral factor. Eachof the sensors may be selected from the group consisting of, forexample, a laser light opacity sensor, a light scattering sensor, aparticle ionization sensor, a particle acoustic measurement sensor, andan electrostatic precipitation sensor.

In a third embodiment, a method of generating a particulate matter (PM)or particle number (PN) is provided. The method includes receivingreadings of an exhaust sample from at least three different sensors,applying a union function to the readings, applying an intersectfunction to the readings, and identifying a quantity of a pollutantwithin the exhaust sample. The exhaust sample is linearly transportedthrough the sensors. Each of the readings includes at least one of a PMand a PN. The quantity may include a mass of particles, a number ofparticles, or a concentration of particles. The identifying may be basedon at least one parameter associated with another exhaust sample. Themethod may include filtering the readings from the sensors prior toapplying the union function or the intersect function. The method mayinclude triangulation of the readings.

In a fourth embodiment, an emissions measurement system is provided. Theemissions measurement system includes a sensor cartridge defining anemissions sample inlet and an emissions sample outlet, at least threesensors disposed within the sensor cartridge between the emissionssample inlet and emissions sample outlet, a sample probe that isfluidically connected to the emissions sample inlet, and a batterydisposed in the sensor cartridge that is configured to provide power tothe sensors. The sensors are sequentially connected in a lineararrangement. Each of the sensors is configured to perform a differentmeasurement of a sample.

Each of the sensors may be selected from the group consisting of, forexample, a laser light opacity sensor, a light scattering sensor, aparticle ionization sensor, a particle acoustic measurement sensor, andan electrostatic precipitation sensor. In an instance, the sensorsinclude a laser light opacity sensor, a light scattering sensor, and aparticle ionization sensor. One of the sensors may be a laser lightopacity sensor configured to use a blue laser.

The sensors may be configured to be synchronized. A processing unit maybe wirelessly connected to the sensors. The processing unit may beconfigured to provide results based on data provided by the sensors. Theprocessing unit may be configured to triangulate the data provided bythe sensors.

A temperature in any of the sensors may be equal thereby reducing watervapor and condensation buildup.

The sensor cartridge may include shock absorbing materials disposed inthe sensor cartridge. The sensor cartridge may be configured to beconnected to an exhaust of an internal combustion engine.

In a fifth embodiment, a method of measuring emissions is provided. Themethod includes directing an emissions sample into a sensor cartridge,linearly transporting the emissions sample through at least threesensors, directing the emissions sample out of the sensor cartridge,transmitting data from the sensors to a processing unit, and calculatingeither a particle number (PN) or particulate matter (PM) measurement forthe emissions sample using data from the sensors. Each of the sensors isconfigured to perform a different measurement of the emissions sample.

The method may further include triangulating the data from the at leastthree sensors using the processing unit. The calculating may use aproportionality factor, a weighted linear integral factor, or anon-linear integral factor. Each of the sensors may be selected from thegroup consisting of, for example, a laser light opacity sensor, a lightscattering sensor, a particle ionization sensor, a particle acousticmeasurement sensor, and an electrostatic precipitation sensor.

DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the nature and objects of the disclosure,reference should be made to the following detailed description taken inconjunction with the accompanying drawings, in which:

FIG. 1 is a schematic of a first embodiment of the improved PM/PNemissions measurement system mounted to an exhaust stack or tailpipe inaccordance with the present disclosure;

FIG. 2 is a schematic of the disclosure shown in FIG. 1 in a firstconfiguration mounted to an exhaust stack in accordance with the presentdisclosure;

FIG. 3 is a top perspective view of the PM/PN sensor cartridge shown inFIG. 2 in a first configuration;

FIG. 4 is a schematic of the PM/PN sensor cartridge shown in FIG. 2 in asecond configuration;

FIG. 5 is a schematic of an embodiment of a laser light opacity sensor;

FIG. 6 is a schematic of an embodiment of a light-scattering sensor;

FIG. 7 is a schematic of an embodiment of an ionization sensor;

FIG. 8 is a schematic of another embodiment of a laser light opacitysensor;

FIG. 9 is a schematic of an embodiment of an acoustic sensor;

FIG. 10 is a schematic of another embodiment of an acoustic sensor;

FIG. 11 is a schematic of an embodiment of the processing unit shown inFIG. 1;

FIG. 12 is a schematic of an embodiment of the wireless engine computerinterface unit shown in FIG. 1;

FIG. 13A-13D illustrate analysis of data from sensors in accordance withthe present disclosure; and

FIG. 14 is a flowchart illustrating an embodiment of software operationin accordance with the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Although claimed subject matter will be described in terms of certainembodiments, other embodiments, including embodiments that do notprovide all of the benefits and features set forth herein, are alsowithin the scope of this disclosure. Various structural, logical,process step, and electronic changes may be made without departing fromthe scope of the disclosure. Accordingly, the scope of the disclosure isdefined only by reference to the appended claims.

The embodiments of the measurement device disclosed herein use three ormore “triangulated” complementing and sufficiently accurate PM/PNsensing techniques in a compact and lightweight design, supplementedwith software algorithms, to capture the nuances of vehicle exhaustPM/PN otherwise missed by traditional PEMS-based PM/PN measurementsystems. The triangulation technique (collecting three or more separatemeasures of different particulate properties and integrating these toprovide a multidimensional description of the sampled particulate) canprovide a number of advantages over conventional single metric PM/PNmeasurement-based systems because it creates a superior, more robustbasis for measurement. Further, the underlying logic behind thetriangulation approach is the premise that no single method will everadequately describe a complex and variable analyte like PM/PN, and thatmultiple complementary measures of the same sample mapping theirsimilarities and dissimilarities provide a superior description of thePM/PN. Further, the principle can be extended to calibration to providesurrogate measures for instrumental techniques less readily deployed aspart of mobile PEMS measurement work. Mapping three or more sensorresponses onto the response profile of a conventional measurement methodprovides a mechanism for the generation of “reference measurement like”data, which are typically superior to a conventional direct referencemethod calibration, even using the most similar of the sensor set.

The availability of three or more data sets also allows for routinefault testing. While each sensor will respond differently toparticulate, they will each exhibit behaviors that can be mapped ontobehavior for two or more other sensors. As a sensor begins to fail orafter a sensor is damaged, its response characteristics will no longermap onto other sensor behavior in the same fashion, indicating aproblem. Perhaps the easiest way to envision this is to consider athree-sensor array comprising opacity, light scattering, and ionizationsensors. The opacity sensor would be more responsive to coarsermaterial, the ionization sensor would be more responsive to finermaterial, and the light scattering sensor response would sit somewherebetween the other two. The light scattering sensor response could bemapped onto the other two sensors' responses, both individually and incombination. While the associated relationships would be not absolute,these and associated degrees of agreement could also be determined alongwith baseline and noise characteristics, and used to benchmark sensorperformance and provide an early indication of atypical behavior, itselfa potential indicator of sensor failure or sampling issues. Thus, threeor more data sets may allow determination that a sensor is faulty orprovide sufficiently accurate results even if a sensor is damaged orfailing.

Disclosed herein is a small, lightweight, sample-parcel synchronizedmulti-chamber device to measure PM and PN concentration from internalcombustion engines (ICE) and other sources of particulate. The sensordesign has the ability to obtain second-by-second PM/PN concentrationfrom the ICE in, for example, either a dedicated “pass/fail”lane-testing configuration or as a field unit. The device can be usedin, for example, roadside vehicle stop-and-spot-check testing procedures(so-called “snap-acceleration testing”), conventional engine or chassisdynamometer testing with or without sample dilution, and PEMS stylein-vehicle mobile emission measurement work. The embodiments disclosedherein provide several unique, complementing, and simultaneousmeasurements of the sample parcel as it passes through a common sensorcartridge for a more accurate and multi-dimensional observation of thesample parcel. The sample may be air, exhaust gas and/or condensatesample. The embodiments disclosed herein also reduce the physicalfootprint of the device (relative to existing systems) to allow thedevice to be easily attached to the frame, exhaust, or stack of thevehicle in order to: reduce as much distance as possible between theexhaust outlet and the device inlet to reduce sample degradation, heatloss, and water condensation; reduce weight so as to safely transportand attach the device without damaging the frame, exhaust, or stack ofthe vehicle; and/or rapidly attach, remove, and redeploy the devicebetween vehicles in order to increase the throughput of individual testsand/or test vehicles. The embodiments disclosed herein also reduce theamount of power consumption typically required of existing samplesystems by removing the power demand associated with resistance heat forconditioning sample lines and housing chambers.

The embodiments disclosed herein also increase device operating timecompared to conventional PEMS PM/PN measurement systems because thislower energy demand system can be operated on battery power for longerperiods of time. Furthermore, the potential to run the device forseveral hours on internal battery power means the device can be used insituations where external power supplies, for example access from thevehicle's own electrical system or a dedicated on-board generator orexternal battery unit, are not feasible.

The embodiments disclosed herein also consolidate the multiple samplesensors into a common, rapidly replaceable cartridge in order toreduce/eliminate downtime typically associated with existing measurementsystems. The embodiments disclosed herein also can wirelessly transmitsecond-by-second data from the sensors directly to a computer, tablet,smart phone, or other device in order to provide data/informationregarding the measured variables and measuring equipment status inreal-time while further reducing installation time required forhard-wired communications.

As used herein, “exhaust” can refer to matter expelled from a tailpipeor stack and “emissions” can refer to the specific pollutants in theexhaust. Emissions may be further differentiated using terms such as“exhaust emissions” or “tailpipe emissions” to distinguish betweenexhaust emissions, evaporative emissions, or crankcase emissions.However, the term emissions generally refers to any type of exhaust.

The real-world operating conditions of an ICE are constantly changingdue to constantly-changing engine power demand, which in turn means theexhaust constituents change from one instant to another. For example,instantaneous driver behavior (e.g., how hard the driver presses down onthe accelerator or when the driver typically changes gears) influences awide range of factors, including the instantaneous engine revolutionsper minute (RPM), intake manifold absolute pressure (MAP), andair-to-fuel ratio (AFR). In addition, there are many other design andoperating parameters, which do not change instantaneously, but that canalso affect the exhaust gas character. Some examples are engine size,engine tuning, fuel type, vehicle maintenance condition, post-combustionemissions controls, and ambient conditions. As a result, PM/PNcharacteristics (e.g., size distribution, elemental carbon content,average composition of adsorbed pollutants, etc.) are complex and highlydynamic. Traditional PM/PN instrument design trends have favored thestep-wise modification of existing laboratory instrumentation for use inPEMS-style application, and the continuous improvement on a single,specific (selected) sensor technique as a mechanism to increase singlemetric measurement accuracy. Unfortunately, the associated instrumentand sensor solutions are typically much larger and arguably moreover-engineered than purpose-built counterparts. The financial logicbehind this trend is straightforward: it is easier and more costeffective for a manufacturer to do minimal work to retrofit an existinglaboratory instrument for on-road testing than it is to build dedicateddevice. The trade-off, however, for such conservative manufacturingstrategies is that the “state-of-the-art” systems tend to be large,cumbersome devices with relatively large power demands which are oftenpoorly suited to their application. As mentioned above, a sensory arraymeasurement strategy based on several differing sensing techniques is ahighly-informative technique.

It was not expected that a dissimilar set of smaller and less accuratesensors could provide an adequate PM/PN description. However, inaddition to a reduced power demand and a smaller footprint device thatis more readily deployed in a much wider range of emission measurementapplications, the combination of several simplified“non-state-of-the-art” low-tech, less-accurate sensor devices into anarray would, while arguably providing a less accurate direct measure ofany one PM/PN metric, provide a multidimensional PM/PN description whichwhen considered in combination can provide a more comprehensive recordof PM/PN emissions. Therefore, the use of less accurate sensors canprovide not only a measurement of the amount of particulate emitted froma vehicle, but also a set of diagnostics that may help to identify thenature of the emissions, or even the specific engine or exhaust systemissue that led to that emission increase.

So, rather than a very accurate measure of one metric, which is by thenature of PM/PN unlikely to be the single most informative option of allsituations, embodiments of the device disclosed herein are intended toprovide several less accurate but complementary and arguably much moreall-encompassing measures of any given PM/PN sample.

Additionally, a feature of this device is that these sensors can measurethe same sample parcel in series rather than simultaneously samplingfrom the same exhaust and measuring in parallel. Thus, the sensors arefluidically connected in series. As the same sample is “seen” by eachsensor in turn, the separate sensor data time-series can be aligned andintegrated to provide more information in real-time (or near-real-time)than less well aligned sampling configurations (e.g., separate sensorsdeployed in configurations where offsets were unknown or parallelconfigurations in which the sensors sampled from slightly differentexhaust sampling points).

Also, an embodiment of the device disclosed herein can make use of laserlight which is farther from the infrared wavelength (e.g., spectral peakof 550-570 nm) than lasers typically used in competing measurementsystems (e.g., spectral peak of 660 nm) to increase the inherent signalto noise ratio of the system, thereby increasing its sensitivityrelative to other opacity and scattering systems if they were simplyreduced to a size similar to these components.

In short, it would not be assumed that multiple less accurate sensorscould provide a more accurate measurement of any single metric, thetraditional goal of many previous instrument development programs andthe USEPA (PM) and the EU (PN) regulatory strategies. However, anapproach based on the integration of multiple reasonably accuratemeasurements of complementary metrics can provide robust measure ofmultiple metrics, a more comprehensive description of PM/PN, and therebyan ability to incorporate measures that will be sensitive to thechanging nature of PM/PN emissions

In effect, these various sensor advantages and strengths may not onlycancel out the weaknesses of the other sensors, but actually reinforceeach other and provide a superior view of the actual instantaneouscharacter of PM/PN emissions in real-time. Therefore, simultaneouslycapturing different characteristics of particulate matter usingdifferent sensing techniques of the exhaust sample provides a unique,individual signature of the constantly varying “collective”characteristic of the particulate matter emissions sample. The system iscapable of accurately differentiating the size, striation (e.g.,“category” of pollutant), and composition (e.g., make up of pollutant)of PM/PN by synchronizing dissimilarly configured sensors.

Recent analysis of multiple sensors attuned to measuring the same sampleparcel has indicated that the simultaneous multi-dimensional measurementprovides a more accurate, triangulated image of the PM/PN specifics suchas, for example, particle distribution, size, coarse/fine attributes,and potentially even mass. This is illustrated in FIGS. 13A-13D.

In FIG. 13A and FIG. 13B, an embodiment of the device disclosed hereinusing a single scattering sensor (labeled “parSYNC1”) is compared to areference method (MAHA MPM4 PM Analyzer). In the example used, thedegree of agreement was 75% (Pearson correlation coefficient, R, 0.75).During the same work, similar and less strong agreement were observedfor the opacity and ionization sensors, respectively, namely 75% and42%. If any of these measurement data series were used individually,they would only provide an approximate indication of the referencemethod output. Predictive power is improved if the sensor outputs areused in combination.

A triangle plot of the relative responses of three sensors (opacity,scattering and ionization) to particulates in vehicle exhaust gassampled as the vehicle's engine is seen in FIG. 13C. FIG. 13Cillustrates the multidimensional nature of the sample. The individualsensors respond to subtly different characteristics of the particulate.

Consistent with this observation, an integrated two sensor (opacity andlight scattering) solution, denoted by the term parSYNC*, providedimproved agreement with the same reference method, namely 92% (FIG. 13B,Pearson correlation coefficient, R, 0.92) compared with the 75%agreement both sensors individually provided. This shows how a dataprocessing method that uses the combined signals from different, lesssophisticated sensors can greatly improve the measurement result. ThisparSYNC* is a mathematical model of the relationship between referencemethod and sensor responses non-linearly mapped across several timeintervals (e.g., 3 seconds if the instrument is logging at 1 secondresolution).

The ability of the multiple sensor technique to provide additionaldiagnostic information is illustrated using the example shown in FIG.13D. Here, the fine/coarse split in the same vehicle exhaust particulatesample is estimated using a simple linear proportionality term. Fineparticles equal (parSYNC*)×((ionization sensor measurement)/(sum ofscattering, opacity, and ionization sensor measurements)), whereparSYNC* represents the final, combined (multi-sensor) andpost-processed output of the unit for a given sample or test.

Such a combination of performance and diagnostic information ispresently not attainable in conventional portable systems because of thecompromises or trade-offs required with respect to size, weight, powersupply, or other factors to make a portable system, and the reliance ona single PM/PN measurement technique, which regardless of accuracy canonly provide one dimension of information. Therefore, the totality ofthe multiple, dissimilar, in-line sensors can either be evaluatedseparately on their own merits (e.g. used as a “flagged” event),post-processed at a future date (utilizing a different/new metric set toaccommodate for historical data sets in the future), or combined intoone metric such as parSYNC* above based on a reference method, asecondary measure such as a pass/fail for a vehicle emission performancetest such as the California Smog Check, or an arbitrary numeric scale onwhich such secondary measures can be evaluated. This latter concept isillustrated using the Particulate Synchronization Number (PSN) aweighted sum of sensor outputs, optimized to differentiate pass (low PSNvalues) and fails (high PSN values) relative to a pass/fail threshold.

An opacity sensor approach is generally responsive, robust, and wellaccepted. For example, it provides the basis of the SAE J1667 standardtest. However, an opacity sensor approach is biased toward samples moreheavily loaded with larger particles and black smoke, and is now widelyregarding as an inexact and even unreliable measure of PM/PN for modernvehicles that tend to emit much finer material.

A scattering sensor approach is generally more amenable to emissionsfrom modern vehicles. It is more sensitive to smaller particulate sizefractions. Instrument configurations are robust and can be readilydeployed in a similar fashion to existing opacity systems. However, ithas limited sensitivity to the very finest particulate, a fraction thatis becoming an increasing important component of vehicle emissions.

An ionization sensor approach is generally biased towards these smallerparticles, but is not necessarily as responsive as optical/light-basedsensors or as easily-deployed in more challenging sampling situations.This approach is also highly-sensitive to issues associated with watercondensation.

An acoustic sensor approach, which may incorporate a frequency-based orparticle counter approach, provides an alternative to traditional sensormethods, but may need to be carefully calibrated to routinely providereliable data, which is an issue that would hinder its standalone use inmany inspection vehicle approval schemes.

Such sensor advantages and disadvantages also enable a unique andspecifically-arranged configuration. The embodiments disclosed hereinensure that each sensor is “seeing” the exact same sample parcel. Assuch, the sample chambers are laid out or plumbed in a linear (series),versus simultaneous (parallel), configuration. Thus, the sample chambersand sensors are serially connected in a linear arrangement. A samplewill be transported from a first sensor to a second sensor to a thirdsensor and so on. Such a linear configuration can ensure that particlesof the emissions sample pass through each sensor and the flow rate canbe optimized to maximize the dwell-time sensitivity trade-off. Thus, theflow rate provides enough time in each chamber so each sensor canaccurately measure the sample, but not so much time that the sample hasa chance to change significantly with its surroundings to condense,degrade, decompose, or otherwise interact. Further, a power, heat, andwater vapor management system in a compact space and in close proximityto the sample exhaust may be used to condition the sample to maximizethe measurement accuracy and correctly distinguish the various PM/PNsample signature(s).

The system includes a PM/PN sensor cartridge comprised of multipledifferent and synchronized sensors, a computerized circuit controllerboard, a wireless duplex/transceiver, and a software system designed tocombine the differing sample parcel signatures in real time. A pneumaticpump unit may be used to transport the sample into and out of eachsensor chamber within the sensor cartridge. Other pump designs also maybe used.

The power, heat, and water vapor management system of the device may beintegrated in one sealed housing and chassis. Placement of the onboardbattery/power unit, sample cartridge, main sample tubes, and otherphysical hardware in a single, compact space, and in close proximity toeach other can ensure an optimal, stable, and constant temperaturethroughout all hardware components. Thus, the temperature in each of thesensors may remain approximately equal relative to the temperature inthe other sensors at a given time, as the device's total masstemperature regulates to the ambient temperature plus the additionalwaste heat from the sample air over time (e.g., during testing).Temperature ranges for this device may be approximately −3° C. to 37°C., though other operating temperatures are possible. Maintaining anapproximately equal temperature in each sensor avoids drawbacks of otherdesigns that separate these components by both physical space and/orseparate housings because the entire unit maintains the sameapproximately constant temperature while the sample travels throughvarious sensors and pathways. The high temperature and temperaturestability assists in significantly reducing the water vapor andcondensation buildup that typically occurs in PEMS systems.Additionally, the physical design of the flow pathways can utilizegravitational assistance by creating peak and valley sample tubepositions. This, in turn, enables water condensation to easily collectin the valleys and to be ejected without the use of pumps or otherpowered systems.

Software can be configured to integrate the raw data from the multiplesensors (such as three or more sensor time series) and can be usedindividually, in combination with each other and/or with otherproperties measured by on-board sensors (e.g., sample temperature andhumidity), to provide measures or surrogate measures of different PM/PNmetrics based on secondary calibration principles such as, for example,the comparison of at least one reference parameter measurement. Thequantity within the exhaust sample can be the amount (e.g., mass, numberof particles, concentration, etc.) of a known pollutant within theexhaust sample. The software can utilize mathematical relationshipsdeveloped through comparison trials between the device's onboard sensorsand several external, accepted testing methodologies and standards.These mathematical relationships have been converted into the softwarecode so as to provide the user with the appropriate, seamless comparisondata that corresponds to various laboratory standards.

Further, the targeted parameter can be any property of PM/PN (e.g., asoot metric such as Soot Number, or a PM or PN as measured by aparticular instrument) and the associated surrogate can be produced bymapping sensor inputs as, for example, a non-linear function of theindividual sensor signals, ratios of either the combined signals, or adirect signal from one or more sensors.

The device can obtain sequential/linear measurements of the sampleparcel utilizing multiple measurement capabilities such as opacity,light-scattering, ionization, electrostatic precipitation, and/oracoustic technique(s). At least one measurement, such as three or moredifferent measurements, can be used as the basis for mapping sensorresponses onto a reference measurement. For example, three, four, five,or six sensors can be used together. Other numbers of sensors arepossible. In an instance, two different acoustic techniques, forexample, can be used together in the three or more sensors. In anotherinstance, two light scattering sensors are used, each having a differentcolor laser.

In some instances it may be preferable to utilize a blue laser to avoidpotential heat interference associated with exhaust flow common with redlasers. Red laser light sources are higher energy devices than bluelight sources. A blue laser also may allow for a shorter measurementdistance.

Use of the combined PM/PN measurements can provide a lighter device witha smaller footprint that is cheaper to manufacture.

An embodiment of the device can utilize at least three of the following:a small-scale opacity reading with a laser light wavelength; a miniaturelaser light scattering measurement; a compact ionization measurement;and an acoustic chamber. Other combinations of sensors are possible.

When combined and harmonized, these three or more individual PM/PNmeasurements provide a more accurate view of PM/PN through triangulationby responding uniquely to the chemical and physical variety commonlyfound in any PM/PN sample. Furthermore, in instances where comparisonshave already been made, these combined PM/PN measurements comparefavorably to current industry opacity meter, laser-light scattering, andother industry-accepted measurement techniques.

Different embodiments and/or variations may contain the followingconfigurations: a module for gaseous pollutant measurement (additionalbaseline methodology to establish a secondary ratio based on non-PM/PNpollutants); a secondary light scattering sensor at a differingfrequency to further delineate real time events, based on additionalinput; and/or hardwired power and data harness for the simultaneouslogging of other data sources (for example, OBD2 or CAN data from thevehicle the device is used in combination with for PEMS applications).

The system disclosed herein and illustrated in FIGS. 1 and 2 provides animproved apparatus for sensing PM/PN emissions from exhaust at point 22comprised of a synchronized set of differing PM/PN sensors in a system10 configured to communicate in a wireless 18 or wired manner with aprocessing unit 60, which can also obtain engine data via an engine dataunit 70 connected either in a wireless 50 or wired configuration. Theengine data unit 70 logs engine management data accessed and used by thevehicle's engine management system. These engine management data use thespeed of the engine and engine manifold pressure (RPM and MAP data sets)and other engine diagnostics to calculate properties such as the exhaustgas flow rate within the exhaust system. The engine data unit 70 canalso access the vehicles speed (Hall sensor output) or incorporate aglobal positioning system (GPS) vehicle location and speed data, whichcan be used in combination with exhaust emissions data to morecomprehensively characterize the relationship between vehicle activityand total emissions.

In an example, the cartridge of the system 10 has an exteriorpolycarbonate/plastic shell approximately 193×117×57 mm in dimension,which is waterproof, airtight, sealed, and self-contained. Other shellmaterials or sizes are possible. The shell or housing contains amulti-channel sensor device. The cartridge of the system 10 may weighless than 0.5 kg and may have three main orifices: a sample flow inlet,a sample flow outlet, and an electronic coupler which provides power tothe sensors as well as duplexing capabilities to control the internalsensors and to relay voltages and/or signals. The cartridge can bedesigned for use with quick release friction-and-pressure coupling. Thecartridge coupling array also may utilize shock-absorbing materials(e.g., rubber, neoprene, etc.) and a tension design to create ashock-resistant framework or “cradle” to separate the cartridge arrayfrom the main internal housing. These features all contribute to asingular, easily-replaceable cartridge. A field-replaceable PM/PNcartridge greatly reduces present down-time associated with repairing abad sensor. The cartridge in system 10 can provide for a new sensorreplacement while the old/bad sensor is repaired at a better time and/orlocation.

The source may be comprised of an internal combustion engine 5 andinclude an exhaust tailpipe or stack at point 22. The system 10 maycomprise a sample probe 24 and sample tube 23 extending from the inletport of the system 10 into the exhaust tailpipe or stack at point 22.The source may be a mobile internal combustion engines for passengervehicles, buses, light duty trucks, heavy duty trucks, motorcycles,off-road vehicles, non-road vehicles, farm equipment, constructionequipment, aircraft, locomotives, or water vessels. The source(s) mayalso consist of generators, drainage and irrigation pumps, orcompressors. The source(s) also may be other devices that produceemissions. In an example, a distance between the tailpipe exhaust andthe device inlet is 1 m, which ensures that the device is as close tothe source as possible so as to optimize sample heat and minimizecondensation. The sample tubing in this example is a variation of a highheat silicone with a 3 mm diameter.

The PM/PN exhaust sensor array cartridge 11 may employ multiple,synchronized techniques from a group consisting of, but not limited to:laser light opacity, light scattering, particle ionization, particleacoustic measurement, and electrostatic precipitation. Electrostaticprecipitation is a technique whereby particles are positively chargedand measured on oppositely charged plates. Some of these variousconfigurations are disclosed in FIG. 3 and FIG. 4. Of course, othertypes or sensors or sensor designs also may be used.

In an embodiment, the system 10 uses laser light opacity, laser lightscattering, and particle ionization sensors. When combined, these threesensors compare well with presently accepted and recognized individualsurrogate standards and also combine into an improved triangulation ofdata that allows speciation between various particulate sizes. Othercombinations of sensors are possible and may provide equally improvedresults.

A combination having two, three, or more of the same type of sensor alsomay be used. These three sensors of the same type may have differenttuning, configurations of lasers, or be operated at differentfrequencies. Thus, while different measurement methods in differenttypes of sensors may be used, different parameters or properties in thesame type of sensor also may be used.

The processing unit 60 may have a microprocessor configured to provideemission data as a function of measurements from the PM/PN sensors andthe engine data unit 70. The target measurement of the pollutant mayinclude, but not be limited to particulate mass, number, size,striation, weight, etc. The combined relationship(s) and ratio(s) of themultiple, synchronized PM/PN sensor outputs, when compared to anexternal reference such as a standard laboratory benchmark method, maybe used to impute a surrogate measurement comparable to that of thelaboratory reference from the system 10.

The power source may comprise a battery 12 with an external chargerport. The battery 12 may be a lithium ion battery. However, otherbatteries that provide a small size or weight, durability (e.g.,drainage and recharge capacity, hot/cold temperature performance, otherenvironmental condition performance, etc.), and sufficient high-densitypower storage capacity or an external power supply may be used.

Referring now to the figures, and specifically to FIG. 1 and FIG. 2,this system 10 provides an improved PM/PN emissions measurement systemdepicted as including an exhaust probe 24 attached to an exhaust stackor tailpipe at point 22. The system 10 may be configured to hardwire orwirelessly 18 communicate with a processing unit 60 which may also beobtaining engine data via an engine data unit 70 connected either in awireless 50 or wired configuration. The system 10 also has ananalog-to-digital conversion unit and wireless communications device 14(e.g., a Bluetooth device), a pump 16, a transmitter 17, a baseplate 19used to attach specific electronic components, unit connections 20, andexhaust clamps 21. The baseplate 19 may be fabricated of high-tensilepolycarbonate in an embodiment. Moisture-sensitive electronic componentsand less moisture-sensitive electronic components may be positioned onopposite sides of the baseplate 19. Thus, the baseplate 19 can serve asa moisture barrier. The unit connections 20 may be corrosion-resistantmaterial such as stainless steel, brass, chrome, high-heat silicone, orother material. The unit connections 20 may be configured to facilitatean air-tight seal while transferring the sample from the vehicletailpipe exhaust into the sample tube 23, through the sensor cartridge,through a pump, and exhausted out of the device outlet. The exhaustclamps 21 also may be made of corrosion-resistant material such asstainless steel, brass, chrome, high-heat silicone, or other material.The exhaust clamps 21 are configured to connect the cartridge 11proximate to or on the vehicle exhaust. The sample probe 24 is connectedto the vehicle exhaust at point 22 using, for example, a clamp,adhesives, another mechanical device, or some other fastening method.The analog-to-digital conversion unit and wireless communications device14 also may be separate units in an embodiment. Although a Bluetoothcommunications device is mentioned, other communication devices such asUltra WideBand (UWB) or IEEE 802.11af (White FI) could also be used.

The PM/PN exhaust sensor array cartridge detailed in FIG. 3 and FIG. 4employs a specific linear sequence of sensors 26, 27, 28, 32 undernegative pressure. There is a sample inlet 23 and outlet 31 through afitted seal 9. Each seal 9 may be, for example, a corrosion-resistantbrass and/or stainless steel fitting that can accommodate fitted andsized quick connect/disconnect tubing. Each individual sensor 26, 27,28, 32 is electronically connected via an individual wiring connectionor connections 29 which supply power and/or relay specific voltagereadings to the main wiring harness 30. The main wiring harness 30 canbe coupled and uncoupled via a plug-in connector. It should be notedthat the entire sensor cartridge assembly 11 can be designed to beremoved quickly and efficiently, and replaced in its entirety with anidentical cartridge 11.

Turning back to FIG. 2, the system 10 includes at least three separatePM or PN sensors. The PM or PN sensors are connected to a processingunit 60, which includes a processor and software configured to estimatean overall PM/PN value for an exhaust sample based on PM/PN parametersor signatures associated with the exhaust sample. In an implementation,only a single sensor output and an associated calibration would berequired to provide a PM or PN estimate. However, three or more PM/PNsensor outputs are synchronized with each other and integrated orcombined using, for example, a linear or non-linear function to map theindividual sensor outputs into a singular surrogate number (based onexternally accepted measurement standards) or a “unified property” andmore accurately describe the PM/PN characteristics of the exhaustsample. At least one exhaust parameter may include a proportionalityfactor determined, at least in part, based on amounts of PM/PN withinother previously sampled and calibrated PM/PN exhaust. A weighted linearintegral factor, or a non-linear integral factor may be used instead ofa proportionality factor.

The sensors are in a singular cartridge 11, which is attached to aflexible, shock-resistant suspension band. Quick-disconnect fittings andelectronic ribbon connectors can expedite replacement of the cartridge11 and/or unit 10. The cartridge 11 can be mounted within the unit 10,which can be mounted proximate a source of the exhaust sample. This istypically in a high-vibration or adverse ambient condition. Thecartridge 11 and/or unit 10 can be resistant to dust, liquid water, andsevere weather.

The opacity sensor designs 80 in FIG. 5 and FIG. 8 have been simplifiedto abridge the configuration. This translates to a reduction of weight,space, and power usage with an acceptable or negligible impact toresults. The sample flow 35 enters the sensor 80 through inlet 81 andthe amount of particulate proportionately blocks the laser 25 as thepath of laser light 34 travels to, and is detected by, the photoreceptor33 before the sample exits the sensor 80 through outlet 82. A blue laser25 may be used, as seen in FIG. 5, but an alternate laser 41 (e.g., redlaser, green laser, etc.) is represented in FIG. 8 as a potentialalternative. The exact type of color/emission frequency and range,emission strength, path length, or number of opacity sensors may bevaried to maximize the degree of PM/PN characterization for a givensample.

The light scattering sensor 83 in FIG. 6 has also been simplified toabridge the configuration. This translates to a reduction of weight,space, and power usage with an acceptable or negligible impact toresults. The sample flow 35 enters the sensor 83 through inlet 84. Alaser light source 37 projects a laser 40 at the sample flow 35 pathwayin the external protective and airtight membrane 39. The amount ofparticulate proportionately reflects off of the individual particlesurfaces at an optimal angle 38 and the amount of scattered light isdetected by a photoreceptor 36 positioned to measure light reflectedfrom the laser path 40 while the sample flow 35 exits the sensor 83through outlet 85. A red laser has been represented, but alternatelasers (e.g. blue, green, etc.) also may be used.

The ionization sensor 54 in FIG. 7 has also been simplified to abridgethe configuration. This translates to a reduction of weight, space, andpower usage with an acceptable or negligible impact to results. Thesample flow 35 enters the sensor chamber 56 through inlet 86 and anionization system 55, which may use radiation 53, electrons, RF power,or other mechanisms, positively or negatively charges particles as theyenter the sensor chamber 56. The charge consumed in charging particlesin the sample as it passes through the sensor chamber 56 creates ameasurable voltage or current change, which is detected by theionization system 55 before the sample exits the sensor chamber 56through outlet 87.

The acoustic sensors in FIG. 9 and FIG. 10 are two embodiments of asensor that employs the detection of sounds and/or frequency variations.FIG. 9 features acoustic particle detection whereas FIG. 10 employsacoustic frequency change detection. In both examples, the sample flow35 enters through inlet 88 and exits through outlet 89 in the acousticchamber 44. Each acoustic sensor utilizes a highly sensitive microphone42. However, the embodiment of FIG. 9 utilizes a high-tensile membrane43 to detect particles the sample flow 35 as they collide with themembrane 43. The embodiment of FIG. 10 employs a decreasing funnel 45which induces frequency pitch changes as the flow of particles in thesample flow 35 becomes more turbulent as the particles are forced intoan increasingly narrow pathway.

The sensors in FIGS. 5-10 are examples of the sensors 26, 27, 28, 32 inFIGS. 3-4. The exact combination, order, or configuration of the sensorsin FIGS. 5-10 can vary. The inlets and outlets in FIGS. 5-10 can connectbetween sensors, to the inlet 23, or to the outlet 31. Fluid can flowthrough the inlets and outlets in FIGS. 5-10.

The system 10 in FIG. 2 utilizes a transmitter 17 to communicate data toa processing unit 60 (which may be a “smart device”) in a wired orwireless 18 manner, which is illustrated in FIG. 11. The measurement andstatus data are received via a transmitter/receiver 46 and outgoinginstructions (e.g., calibration instructions, equation modifications,etc.) are sent 18 via a duplexing capability. The processing unit 60 hasa transmitter/receiver 46, a processor 47, and data storage medium 48,with additional input from a user.

The processor 47 can initiate a channel configuration, usingtransmitter/receiver 46, that tells the analog-to-digital conversionunit and Bluetooth communications device 14 how many and which ports onthe analog-to-digital conversion unit and Bluetooth communicationsdevice 14 will be used for measurement. The channel configuration alsoincludes specifications such as voltage or current ranges, speed of dataacquisition (i.e., how many samples per second), the size of the bufferwhere data is stored temporarily, and the duration of data acquisition(e.g., one time or continuous). After the analog-to-digital conversionunit and Bluetooth communications device 14 is configured, the processor47 will begin acquiring measurements based on a user “start” command.The processor 47 continuously watches the transmitter/receiver 46 andwhen data is available, the processor 47 will read and process the dataand thereafter save the results to a file on a storage medium 48, suchas hard drive, flash memory, or other memory device. The processing unitconsists of computing hardware and a graphical user interface (GUI). Theraw voltage signals generated by the system 10 are processed andpresented as individual PM/PN measurements for each sensor and anoverall triangulated value. Other data that are received from the system10 could be temperature, flow, and humidity measurements. The processor47 also can communicate with engine data device 70 using thetransmitter/receiver 46. Depending on the hardware configuration, thetransmitter/receiver 46 in processing unit 60 may be common forcommunication with the system 10 and engine data device 70.

In an another embodiment, at least part of the processing unit 60 isinside the system 10. This may utilize a real-time operating system(RTOS) and the appropriate processor 47 so that the controlling of dataacquisition and computing of results can occur at the data acquisitionpoint. This embedded processing unit 60 may have an onboard data storagemedium 48 and can transmit data wirelessly to another processing unit(not illustrated), which will be external and can have real-time visualupdate. In this case, a loss of Wi-Fi, Bluetooth, or other wirelesssignal does not mean a loss of data, but merely the loss of visualupdates of the data that is being stored by the onboard computerembedded inside the system 10. This alternate embodiment also mayprovide multi-day, unattended data acquisition. The system 10, can bepowered up and down simultaneously with the engine.

An additional source of data illustrated in FIG. 12 may be obtained fromthe engine source via a wired or wireless engine data device 70 byobtaining either engine control unit data or by temporarily orpermanently placed engine sensors. The data is obtained viatransmitter/receiver 49 and processed via an onboard computer 52. Theengine data device then transmits the process engine data either using ahardwired connection or wirelessly 50 using the transmitter/receiver 49.In an example, the engine data device 70 can also have onboard storage51 that creates a real-time backup of the data being received andtransmitted. The engine data device 70 can connect to an engine viasensors or to an onboard engine computer.

The software design is demonstrated through the concepts of unions andintersects as applied to the field of set theory, as shown in FIG. 14.Each sensor is considered to be a set, one that reflects a certain spacein the overall measurement of PM/PN. The function of the software is tocombine these sets based on logical constructs.

Each sensor is “polled” at high speed to acquire, for example, thousandsof raw data measurements per second. The raw data measurements from eachsensor, which may be voltage or current readings, are subjected toanalog filtering and smoothening to remove artifacts of electricaldisturbances. This process is typically accomplished using aconventional signal filtering methods (e.g., a Kalman, Butterworth, orElliptic filter or other similar noise reduction strategy). This causesthe initial raw N measurements to be reduced to M measurements. The“cleaned” measurements from each sensor are then converted to silos ofPM/PN measurements. Thus, for a three sensor configuration, there willbe three measurements each of PM and PN. Potentially none of these isfully correct. These individual “incomplete” measurements are combinedto provide a more complete and more accurate estimate of PM and PN.

Continuing the set field analogue, the process of combining individualPM and PN estimates is based on unions and intersects. The unionfunction defines the overall space and increases the observability of PMand PN estimates during transient operation of the vehicle. Theintersect function or triangulation term can be considered to multiplexor summarize description of the multidimensional entity indicated by thethree synchronized sensor outputs.

While specific sensor designs have been disclosed herein, othervariations or embodiments of the sensors also may be used.

Although the present disclosure has been described with respect to oneor more particular embodiments, it will be understood that otherembodiments of the present disclosure may be made without departing fromthe scope of the present disclosure. Hence, the present disclosure isdeemed limited only by the appended claims and the reasonableinterpretation thereof.

What is claimed is:
 1. An emissions measurement system comprising: anemissions sample inlet; at least three sensors connected to theemissions sample inlet, wherein the sensors are sequentially connectedin a linear arrangement, wherein each of the sensors is configured toperform a different measurement of a sample, and wherein the sensorscomprise a laser light opacity sensor, a light scattering sensor, and aparticle ionization sensor; and an emissions sample outlet connected tothe sensors.
 2. The emissions measurement system of claim 1, wherein thelaser light opacity sensor is configured to use a blue laser.
 3. Theemissions measurement system of claim 1, wherein the sensors areconfigured to be synchronized.
 4. The emissions measurement system ofclaim 1, further comprising a processing unit wirelessly connected tothe sensors.
 5. The emissions measurement system of claim 4, wherein theprocessing unit is configured to provide results based on data providedby the sensors.
 6. The emissions measurement system of claim 5, whereinthe processing unit is configured to triangulate the data provided bythe sensors.
 7. The emissions measurement system of claim 1 furthercomprising: a sensor cartridge defining the emissions sample inlet andthe emissions sample outlet; and a sample probe that is fluidicallyconnected to the emissions sample inlet, wherein the at least threesensors are disposed within the sensor cartridge between the emissionssample inlet and emissions sample outlet.
 8. The emissions measurementsystem of claim 7, wherein a temperature in any of the sensors is equalthereby reducing water vapor and condensation buildup.
 9. The emissionsmeasurement system of claim 7, wherein the sensor cartridge furthercomprises a shock absorbing material disposed in the sensor cartridge.10. The emissions measurement system of claim 7, wherein the sensorcartridge is configured to be connected to an exhaust of an internalcombustion engine.
 11. A method of measuring emissions comprising:linearly transporting an emissions sample through at least threesensors, wherein each of the sensors is configured to perform adifferent measurement of the emissions sample, and wherein the sensorscomprise a laser light opacity sensor, a light scattering sensor, and aparticle ionization sensor; and calculating, using a processing unit, aparticle number (PN) or particulate matter (PM) measurement for theemissions sample using data from the sensors.
 12. The method of claim11, further comprising triangulating the data from the sensors using theprocessing unit.
 13. The method of claim 11, wherein the calculatinguses a proportionality factor, a weighted linear integral factor, or anon-linear integral factor.
 14. The method of claim 11, furthercomprising: receiving readings of an exhaust sample from the sensors atthe processing unit, wherein each of the readings comprises at least oneof the particulate matter and the particle number; applying a unionfunction to the readings using the processing unit; applying anintersect function to the readings using the processing unit; andidentifying a quantity of a pollutant within the exhaust sample usingthe processing unit.
 15. The method of claim 14, wherein the quantity ofthe pollutant within the exhaust sample comprises a mass of particles, anumber of particles, or a concentration of particles.
 16. The method ofclaim 14, further comprising filtering the readings of the exhaustsample from the sensors prior to applying the union function or theintersect function using the processing unit.
 17. The method of claim14, wherein the identifying is based on at least one parameterassociated with another exhaust sample.
 18. The method of claim 14,further comprising triangulating the readings of the exhaust sampleusing the processing unit.
 19. The method of claim 11, furthercomprising: directing the emissions sample into an emissions sampleinlet defined by a sensor cartridge; and directing the emissions sampleout of an emissions sample outlet defined by the sensor cartridge. 20.The method of claim 19, wherein the sensors are disposed in the sensorcartridge.